The CES approach is described in detail in [1]}, which provides a justification and contextualization of the approach in the literature on data assimilation and Bayesian calibration. If uncertainty quantification is not required, the calibration step provides an effective, derivative-free parameter estimation method, and the emulation and sampling stages are not required. However, when uncertainty quantification is required, the role of the calibration stage is to provide a good set of training points in the vicinity of the posterior mean or mode, to train the emulator in the next stage of the algorithm.
